Disparities in spatially variable gene calling highlight the need for benchmarking spatial transcriptomics methods

Identifying spatially variable genes (SVGs) is a key step in the analysis of spatially resolved transcriptomics data. SVGs provide biological insights by defining transcriptomic differences within tissues, which was previously unachievable using RNA-sequencing technologies. However, the increasing n...

Full description

Saved in:
Bibliographic Details
Published inGenome Biology Vol. 24; no. 1; pp. 1 - 209
Main Authors Charitakis, Natalie, Salim, Agus, Piers, Adam T., Watt, Kevin I., Porrello, Enzo R., Elliott, David A., Ramialison, Mirana
Format Journal Article
LanguageEnglish
Published London BioMed Central 18.09.2023
BMC
Subjects
Cancer
Datasets
Gene expression
Genetic diversity
Mathematical models
Short Report
Transcriptomics
Online AccessGet full text

Cover

More Information
Summary:Identifying spatially variable genes (SVGs) is a key step in the analysis of spatially resolved transcriptomics data. SVGs provide biological insights by defining transcriptomic differences within tissues, which was previously unachievable using RNA-sequencing technologies. However, the increasing number of published tools designed to define SVG sets currently lack benchmarking methods to accurately assess performance. This study compares results of 6 purpose-built packages for SVG identification across 9 public and 5 simulated datasets and highlights discrepancies between results. Additional tools for generation of simulated data and development of benchmarking methods are required to improve methods for identifying SVGs.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1474-760X
1474-7596
1474-760X
DOI:10.1186/s13059-023-03045-1